Overview

Dataset statistics

Number of variables31
Number of observations278587
Missing cells24663
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory65.9 MiB
Average record size in memory248.0 B

Variable types

Numeric7
Text13
DateTime3
Categorical6
Unsupported2

Alerts

ACCESSORIAL_AMOUNT is highly overall correlated with ACCESSORIAL_CHARGE_AMOUNT and 1 other fieldsHigh correlation
ACCESSORIAL_CHARGE_AMOUNT is highly overall correlated with ACCESSORIAL_AMOUNT and 1 other fieldsHigh correlation
AMOUNT_PAID is highly overall correlated with ACCESSORIAL_AMOUNT and 1 other fieldsHigh correlation
BILL_TYPE is highly overall correlated with SHIP_WEIGHT and 1 other fieldsHigh correlation
IO_CODE is highly overall correlated with IO_CODE_DESCRIPTION and 1 other fieldsHigh correlation
IO_CODE_DESCRIPTION is highly overall correlated with IO_CODE and 1 other fieldsHigh correlation
SHIP_WEIGHT is highly overall correlated with BILL_TYPEHigh correlation
SOLAR_MODE is highly overall correlated with BILL_TYPEHigh correlation
Unnamed: 0 is highly overall correlated with IO_CODE and 1 other fieldsHigh correlation
BILL_TYPE is highly imbalanced (52.3%)Imbalance
SHIPPER_NAME is highly imbalanced (58.1%)Imbalance
DESTINATION_COUNTRY_CODE has 2813 (1.0%) missing valuesMissing
DESTINATION_STATE has 3286 (1.2%) missing valuesMissing
ORIGIN_STATE has 6996 (2.5%) missing valuesMissing
PRO_NUMBER2 has 9317 (3.3%) missing valuesMissing
ACCESSORIAL_AMOUNT is highly skewed (γ1 = 41.83294079)Skewed
ACCESSORIAL_CHARGE_AMOUNT is highly skewed (γ1 = 22.23821765)Skewed
AMOUNT_PAID is highly skewed (γ1 = 26.05638454)Skewed
SHIP_WEIGHT is highly skewed (γ1 = 25.84201104)Skewed
Unnamed: 0 is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
DESTINATION_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
ORIGIN_ZIP is an unsupported type, check if it needs cleaning or further analysisUnsupported
MILEAGE has 128368 (46.1%) zerosZeros
SHIP_WEIGHT has 51581 (18.5%) zerosZeros

Reproduction

Analysis started2024-02-16 06:34:07.856772
Analysis finished2024-02-16 06:34:49.584754
Duration41.73 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct278587
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean139293
Minimum0
Maximum278586
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:49.734405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13929.3
Q169646.5
median139293
Q3208939.5
95-th percentile264656.7
Maximum278586
Range278586
Interquartile range (IQR)139293

Descriptive statistics

Standard deviation80421.284
Coefficient of variation (CV)0.57735338
Kurtosis-1.2
Mean139293
Median Absolute Deviation (MAD)69647
Skewness1.1307088 × 10-15
Sum3.8805219 × 1010
Variance6.4675829 × 109
MonotonicityStrictly increasing
2024-02-15T22:34:49.893136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
185722 1
 
< 0.1%
185728 1
 
< 0.1%
185727 1
 
< 0.1%
185726 1
 
< 0.1%
185725 1
 
< 0.1%
185724 1
 
< 0.1%
185723 1
 
< 0.1%
185721 1
 
< 0.1%
187175 1
 
< 0.1%
Other values (278577) 278577
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
278586 1
< 0.1%
278585 1
< 0.1%
278584 1
< 0.1%
278583 1
< 0.1%
278582 1
< 0.1%
278581 1
< 0.1%
278580 1
< 0.1%
278579 1
< 0.1%
278578 1
< 0.1%
278577 1
< 0.1%

ACCESSORIAL_AMOUNT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct41989
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1758.7773
Minimum0.01314
Maximum896151.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:50.045541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01314
5-th percentile15.5052
Q152.56
median233.09046
Q3917.64504
95-th percentile6718.3243
Maximum896151.99
Range896151.98
Interquartile range (IQR)865.08504

Descriptive statistics

Standard deviation13026.212
Coefficient of variation (CV)7.4064019
Kurtosis2312.6202
Mean1758.7773
Median Absolute Deviation (MAD)209.33334
Skewness41.832941
Sum4.8997249 × 108
Variance1.6968219 × 108
MonotonicityNot monotonic
2024-02-15T22:34:50.185506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
68.328 6096
 
2.2%
91.98 1361
 
0.5%
65.7 859
 
0.3%
51.7716 820
 
0.3%
64.55682 721
 
0.3%
785.6406 645
 
0.2%
328.5 441
 
0.2%
52.56 412
 
0.1%
131.4 406
 
0.1%
459.9 385
 
0.1%
Other values (41979) 266441
95.6%
ValueCountFrequency (%)
0.01314 2
< 0.1%
0.07884 1
 
< 0.1%
1.0512 1
 
< 0.1%
1.15632 1
 
< 0.1%
1.26144 3
< 0.1%
1.314 2
< 0.1%
1.52424 1
 
< 0.1%
1.53738 1
 
< 0.1%
1.5768 1
 
< 0.1%
1.6425 1
 
< 0.1%
ValueCountFrequency (%)
896151.9946 12
< 0.1%
846140.4319 12
< 0.1%
655493.9589 8
< 0.1%
592760.6161 6
< 0.1%
563688.5107 8
< 0.1%
533514.695 6
< 0.1%
448882.9772 12
< 0.1%
419682.4677 5
< 0.1%
392183.01 11
< 0.1%
353885.4945 4
 
< 0.1%

ACCESSORIAL_CHARGE_AMOUNT
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct42057
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean472.41597
Minimum0.01314
Maximum129950.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:50.335386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01314
5-th percentile11.79972
Q123.54688
median51.41682
Q3166.10931
95-th percentile1422.3748
Maximum129950.04
Range129950.03
Interquartile range (IQR)142.56243

Descriptive statistics

Standard deviation3259.0799
Coefficient of variation (CV)6.8987506
Kurtosis622.73217
Mean472.41597
Median Absolute Deviation (MAD)35.64882
Skewness22.238218
Sum1.3160895 × 108
Variance10621602
MonotonicityNot monotonic
2024-02-15T22:34:50.484138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.28 12505
 
4.5%
13.14 12303
 
4.4%
19.71 7513
 
2.7%
51.41682 5962
 
2.1%
91.98 5917
 
2.1%
24.966 4872
 
1.7%
45.99 4305
 
1.5%
131.4 3964
 
1.4%
6.57 3631
 
1.3%
65.7 3005
 
1.1%
Other values (42047) 214610
77.0%
ValueCountFrequency (%)
0.01314 234
0.1%
0.02628 1
 
< 0.1%
0.03942 6
 
< 0.1%
0.07884 4
 
< 0.1%
0.09198 1
 
< 0.1%
0.11826 1
 
< 0.1%
0.1314 2
 
< 0.1%
0.14454 1
 
< 0.1%
0.17082 2
 
< 0.1%
0.18396 1
 
< 0.1%
ValueCountFrequency (%)
129950.0404 1
< 0.1%
129943.6544 1
< 0.1%
129775.239 1
< 0.1%
128271.8128 1
< 0.1%
127680.5128 1
< 0.1%
125749.8 1
< 0.1%
125746.9092 2
< 0.1%
125621.9872 2
< 0.1%
125317.8751 1
< 0.1%
123950.3296 1
< 0.1%
Distinct138
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:50.664183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters557174
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowFS
2nd rowFS
3rd rowFS
4th rowFS
5th rowFS
ValueCountFrequency (%)
fs 128128
46.0%
br 21290
 
7.6%
ms 19033
 
6.8%
ha 14551
 
5.2%
pc 10135
 
3.6%
du 9265
 
3.3%
th 7685
 
2.8%
pf 7010
 
2.5%
mn 6112
 
2.2%
wv 5199
 
1.9%
Other values (128) 50179
 
18.0%
2024-02-15T22:34:50.941487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 154107
27.7%
F 141198
25.3%
M 30497
 
5.5%
H 28364
 
5.1%
A 27607
 
5.0%
R 23782
 
4.3%
B 22672
 
4.1%
P 20884
 
3.7%
T 20631
 
3.7%
D 19487
 
3.5%
Other values (14) 67945
12.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 557174
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 154107
27.7%
F 141198
25.3%
M 30497
 
5.5%
H 28364
 
5.1%
A 27607
 
5.0%
R 23782
 
4.3%
B 22672
 
4.1%
P 20884
 
3.7%
T 20631
 
3.7%
D 19487
 
3.5%
Other values (14) 67945
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 557174
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 154107
27.7%
F 141198
25.3%
M 30497
 
5.5%
H 28364
 
5.1%
A 27607
 
5.0%
R 23782
 
4.3%
B 22672
 
4.1%
P 20884
 
3.7%
T 20631
 
3.7%
D 19487
 
3.5%
Other values (14) 67945
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 557174
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 154107
27.7%
F 141198
25.3%
M 30497
 
5.5%
H 28364
 
5.1%
A 27607
 
5.0%
R 23782
 
4.3%
B 22672
 
4.1%
P 20884
 
3.7%
T 20631
 
3.7%
D 19487
 
3.5%
Other values (14) 67945
12.2%
Distinct138
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:51.166041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length14.334642
Min length3

Characters and Unicode

Total characters3993445
Distinct characters32
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)< 0.1%

Sample

1st rowFUEL SURCHARGE
2nd rowFUEL SURCHARGE
3rd rowFUEL SURCHARGE
4th rowFUEL SURCHARGE
5th rowFUEL SURCHARGE
ValueCountFrequency (%)
surcharge 128430
22.5%
fuel 128128
22.4%
charge 28741
 
5.0%
brokerage 21293
 
3.7%
custom 21290
 
3.7%
msc 19033
 
3.3%
chrgs 19033
 
3.3%
handling 14551
 
2.5%
fee 12452
 
2.2%
duty 11939
 
2.1%
Other values (203) 166195
29.1%
2024-02-15T22:34:51.534656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 505318
12.7%
R 453241
11.3%
U 310563
 
7.8%
292498
 
7.3%
A 289557
 
7.3%
C 288302
 
7.2%
S 260540
 
6.5%
G 250825
 
6.3%
H 216337
 
5.4%
L 175857
 
4.4%
Other values (22) 950407
23.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 3687121
92.3%
Space Separator 292498
 
7.3%
Other Punctuation 13235
 
0.3%
Dash Punctuation 501
 
< 0.1%
Open Punctuation 45
 
< 0.1%
Close Punctuation 45
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 505318
13.7%
R 453241
12.3%
U 310563
8.4%
A 289557
 
7.9%
C 288302
 
7.8%
S 260540
 
7.1%
G 250825
 
6.8%
H 216337
 
5.9%
L 175857
 
4.8%
F 172995
 
4.7%
Other values (16) 763586
20.7%
Other Punctuation
ValueCountFrequency (%)
/ 13227
99.9%
& 8
 
0.1%
Space Separator
ValueCountFrequency (%)
292498
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 501
100.0%
Open Punctuation
ValueCountFrequency (%)
( 45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 45
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3687121
92.3%
Common 306324
 
7.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 505318
13.7%
R 453241
12.3%
U 310563
8.4%
A 289557
 
7.9%
C 288302
 
7.8%
S 260540
 
7.1%
G 250825
 
6.8%
H 216337
 
5.9%
L 175857
 
4.8%
F 172995
 
4.7%
Other values (16) 763586
20.7%
Common
ValueCountFrequency (%)
292498
95.5%
/ 13227
 
4.3%
- 501
 
0.2%
( 45
 
< 0.1%
) 45
 
< 0.1%
& 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3993445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 505318
12.7%
R 453241
11.3%
U 310563
 
7.8%
292498
 
7.3%
A 289557
 
7.3%
C 288302
 
7.2%
S 260540
 
6.5%
G 250825
 
6.3%
H 216337
 
5.4%
L 175857
 
4.4%
Other values (22) 950407
23.8%

AMOUNT_PAID
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct62830
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3580.7642
Minimum0.01314
Maximum934680.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:51.692288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.01314
5-th percentile111.40092
Q1222.32223
median734.7231
Q32479.4654
95-th percentile11885.13
Maximum934680.96
Range934680.94
Interquartile range (IQR)2257.1432

Descriptive statistics

Standard deviation16659.259
Coefficient of variation (CV)4.6524313
Kurtosis1036.671
Mean3580.7642
Median Absolute Deviation (MAD)590.09112
Skewness26.056385
Sum9.9755435 × 108
Variance2.7753092 × 108
MonotonicityNot monotonic
2024-02-15T22:34:51.826360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91.98 1324
 
0.5%
51.7716 816
 
0.3%
64.55682 719
 
0.3%
785.6406 649
 
0.2%
1859.31 564
 
0.2%
429.678 558
 
0.2%
65.7 522
 
0.2%
2168.1 428
 
0.2%
96.40818 321
 
0.1%
96.579 288
 
0.1%
Other values (62820) 272398
97.8%
ValueCountFrequency (%)
0.01314 1
 
< 0.1%
0.07884 1
 
< 0.1%
1.314 1
 
< 0.1%
6.57 3
< 0.1%
6.59628 1
 
< 0.1%
7.34526 1
 
< 0.1%
8.6067 1
 
< 0.1%
8.65926 1
 
< 0.1%
9.90756 1
 
< 0.1%
11.826 1
 
< 0.1%
ValueCountFrequency (%)
934680.958 1
 
< 0.1%
896151.9946 12
< 0.1%
846140.4319 12
< 0.1%
802120.1836 4
 
< 0.1%
655493.9589 8
< 0.1%
592760.6161 6
< 0.1%
563688.5501 8
< 0.1%
555631.1546 3
 
< 0.1%
533514.695 6
< 0.1%
461741.0323 6
< 0.1%
Distinct1497
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2019-03-26 00:00:00
Maximum2024-02-05 00:00:00
2024-02-15T22:34:51.969723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:52.115993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

BILL_TYPE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
LINEHAUL
225824 
BALANCE DUE
50068 
SEP. BILLED ACC.
 
2695

Length

Max length16
Median length8
Mean length8.6165543
Min length8

Characters and Unicode

Total characters2400460
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLINEHAUL
2nd rowLINEHAUL
3rd rowLINEHAUL
4th rowLINEHAUL
5th rowLINEHAUL

Common Values

ValueCountFrequency (%)
LINEHAUL 225824
81.1%
BALANCE DUE 50068
 
18.0%
SEP. BILLED ACC. 2695
 
1.0%

Length

2024-02-15T22:34:52.262093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T22:34:52.375566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
linehaul 225824
67.6%
balance 50068
 
15.0%
due 50068
 
15.0%
sep 2695
 
0.8%
billed 2695
 
0.8%
acc 2695
 
0.8%

Most occurring characters

ValueCountFrequency (%)
L 507106
21.1%
E 331350
13.8%
A 328655
13.7%
N 275892
11.5%
U 275892
11.5%
I 228519
9.5%
H 225824
9.4%
C 55458
 
2.3%
55458
 
2.3%
B 52763
 
2.2%
Other values (4) 63543
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2339612
97.5%
Space Separator 55458
 
2.3%
Other Punctuation 5390
 
0.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 507106
21.7%
E 331350
14.2%
A 328655
14.0%
N 275892
11.8%
U 275892
11.8%
I 228519
9.8%
H 225824
9.7%
C 55458
 
2.4%
B 52763
 
2.3%
D 52763
 
2.3%
Other values (2) 5390
 
0.2%
Space Separator
ValueCountFrequency (%)
55458
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5390
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2339612
97.5%
Common 60848
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 507106
21.7%
E 331350
14.2%
A 328655
14.0%
N 275892
11.8%
U 275892
11.8%
I 228519
9.8%
H 225824
9.7%
C 55458
 
2.4%
B 52763
 
2.3%
D 52763
 
2.3%
Other values (2) 5390
 
0.2%
Common
ValueCountFrequency (%)
55458
91.1%
. 5390
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2400460
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 507106
21.1%
E 331350
13.8%
A 328655
13.7%
N 275892
11.5%
U 275892
11.5%
I 228519
9.5%
H 225824
9.4%
C 55458
 
2.3%
55458
 
2.3%
B 52763
 
2.2%
Other values (4) 63543
 
2.6%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
MOTOR
209718 
AIR
68850 
INTERNATIO
 
19

Length

Max length10
Median length5
Mean length4.5060609
Min length3

Characters and Unicode

Total characters1255330
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMOTOR
2nd rowMOTOR
3rd rowMOTOR
4th rowMOTOR
5th rowMOTOR

Common Values

ValueCountFrequency (%)
MOTOR 209718
75.3%
AIR 68850
 
24.7%
INTERNATIO 19
 
< 0.1%

Length

2024-02-15T22:34:52.505670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T22:34:52.616307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
motor 209718
75.3%
air 68850
 
24.7%
internatio 19
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
O 419455
33.4%
R 278587
22.2%
T 209756
16.7%
M 209718
16.7%
I 68888
 
5.5%
A 68869
 
5.5%
N 38
 
< 0.1%
E 19
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1255330
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 419455
33.4%
R 278587
22.2%
T 209756
16.7%
M 209718
16.7%
I 68888
 
5.5%
A 68869
 
5.5%
N 38
 
< 0.1%
E 19
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 1255330
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 419455
33.4%
R 278587
22.2%
T 209756
16.7%
M 209718
16.7%
I 68888
 
5.5%
A 68869
 
5.5%
N 38
 
< 0.1%
E 19
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1255330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 419455
33.4%
R 278587
22.2%
T 209756
16.7%
M 209718
16.7%
I 68888
 
5.5%
A 68869
 
5.5%
N 38
 
< 0.1%
E 19
 
< 0.1%
Distinct80
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:52.728486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length30
Mean length18.913754
Min length7

Characters and Unicode

Total characters5269126
Distinct characters50
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)< 0.1%

Sample

1st rowYoung Ltd
2nd rowYoung Ltd
3rd rowYoung Ltd
4th rowYoung Ltd
5th rowYoung Ltd
ValueCountFrequency (%)
and 144990
19.5%
hodges 67773
 
9.1%
weaver 67773
 
9.1%
lyons 67773
 
9.1%
taylor-newman 40926
 
5.5%
morales 34991
 
4.7%
anderson-esparza 31439
 
4.2%
torres 30715
 
4.1%
peck 30414
 
4.1%
inc 16618
 
2.2%
Other values (116) 209958
28.2%
2024-02-15T22:34:52.989850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 538645
 
10.2%
464783
 
8.8%
a 461560
 
8.8%
n 442305
 
8.4%
o 402202
 
7.6%
r 384976
 
7.3%
s 354879
 
6.7%
d 285446
 
5.4%
, 144968
 
2.8%
y 134464
 
2.6%
Other values (40) 1654898
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3837165
72.8%
Uppercase Letter 718448
 
13.6%
Space Separator 464783
 
8.8%
Other Punctuation 144968
 
2.8%
Dash Punctuation 103762
 
2.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 538645
14.0%
a 461560
12.0%
n 442305
11.5%
o 402202
10.5%
r 384976
10.0%
s 354879
9.2%
d 285446
7.4%
y 134464
 
3.5%
l 115911
 
3.0%
g 101682
 
2.6%
Other values (15) 615095
16.0%
Uppercase Letter
ValueCountFrequency (%)
L 115629
16.1%
H 78254
10.9%
W 72963
10.2%
T 71731
10.0%
P 52690
 
7.3%
M 43039
 
6.0%
N 40926
 
5.7%
A 38138
 
5.3%
E 36061
 
5.0%
D 31661
 
4.4%
Other values (12) 137356
19.1%
Space Separator
ValueCountFrequency (%)
464783
100.0%
Other Punctuation
ValueCountFrequency (%)
, 144968
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 103762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4555613
86.5%
Common 713513
 
13.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 538645
11.8%
a 461560
 
10.1%
n 442305
 
9.7%
o 402202
 
8.8%
r 384976
 
8.5%
s 354879
 
7.8%
d 285446
 
6.3%
y 134464
 
3.0%
l 115911
 
2.5%
L 115629
 
2.5%
Other values (37) 1319596
29.0%
Common
ValueCountFrequency (%)
464783
65.1%
, 144968
 
20.3%
- 103762
 
14.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5269126
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 538645
 
10.2%
464783
 
8.8%
a 461560
 
8.8%
n 442305
 
8.4%
o 402202
 
7.6%
r 384976
 
7.3%
s 354879
 
6.7%
d 285446
 
5.4%
, 144968
 
2.8%
y 134464
 
2.6%
Other values (40) 1654898
31.4%
Distinct2078
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:53.195428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length8.5738315
Min length3

Characters and Unicode

Total characters2388558
Distinct characters33
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique345 ?
Unique (%)0.1%

Sample

1st rowBROKEN ARROW
2nd rowSAN DIEGO
3rd rowSAN DIEGO
4th rowSAN DIEGO
5th rowSAN DIEGO
ValueCountFrequency (%)
san 99933
24.1%
diego 98645
23.8%
desoto 16431
 
4.0%
fairburn 15427
 
3.7%
bitozeves 14435
 
3.5%
mabank 8955
 
2.2%
zatec 8338
 
2.0%
zebrak 7192
 
1.7%
houston 6758
 
1.6%
gardena 5685
 
1.4%
Other values (2149) 133101
32.1%
2024-02-15T22:34:53.728497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 269633
11.3%
A 244147
10.2%
O 241044
10.1%
N 228250
9.6%
I 187523
 
7.9%
S 179826
 
7.5%
D 141776
 
5.9%
136534
 
5.7%
G 131238
 
5.5%
R 118707
 
5.0%
Other values (23) 509880
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2251966
94.3%
Space Separator 136534
 
5.7%
Open Punctuation 20
 
< 0.1%
Dash Punctuation 16
 
< 0.1%
Other Punctuation 16
 
< 0.1%
Decimal Number 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 269633
12.0%
A 244147
10.8%
O 241044
10.7%
N 228250
10.1%
I 187523
8.3%
S 179826
8.0%
D 141776
 
6.3%
G 131238
 
5.8%
R 118707
 
5.3%
T 79103
 
3.5%
Other values (16) 430719
19.1%
Other Punctuation
ValueCountFrequency (%)
. 10
62.5%
, 6
37.5%
Decimal Number
ValueCountFrequency (%)
9 3
50.0%
0 3
50.0%
Space Separator
ValueCountFrequency (%)
136534
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2251966
94.3%
Common 136592
 
5.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 269633
12.0%
A 244147
10.8%
O 241044
10.7%
N 228250
10.1%
I 187523
8.3%
S 179826
8.0%
D 141776
 
6.3%
G 131238
 
5.8%
R 118707
 
5.3%
T 79103
 
3.5%
Other values (16) 430719
19.1%
Common
ValueCountFrequency (%)
136534
> 99.9%
( 20
 
< 0.1%
- 16
 
< 0.1%
. 10
 
< 0.1%
, 6
 
< 0.1%
9 3
 
< 0.1%
0 3
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2388558
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 269633
11.3%
A 244147
10.2%
O 241044
10.1%
N 228250
9.6%
I 187523
 
7.9%
S 179826
 
7.5%
D 141776
 
5.9%
136534
 
5.7%
G 131238
 
5.5%
R 118707
 
5.0%
Other values (23) 509880
21.3%
Distinct98
Distinct (%)< 0.1%
Missing2813
Missing (%)1.0%
Memory size2.1 MiB
2024-02-15T22:34:53.883122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters551548
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 206598
74.9%
cz 33761
 
12.2%
be 8303
 
3.0%
au 7034
 
2.6%
de 4166
 
1.5%
cn 2966
 
1.1%
gb 2587
 
0.9%
uk 1378
 
0.5%
ca 882
 
0.3%
hk 788
 
0.3%
Other values (88) 7311
 
2.7%
2024-02-15T22:34:54.129166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 215203
39.0%
S 207543
37.6%
C 38135
 
6.9%
Z 34264
 
6.2%
E 13732
 
2.5%
B 11268
 
2.0%
A 8449
 
1.5%
D 5150
 
0.9%
N 3944
 
0.7%
K 3540
 
0.6%
Other values (16) 10320
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 551548
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 215203
39.0%
S 207543
37.6%
C 38135
 
6.9%
Z 34264
 
6.2%
E 13732
 
2.5%
B 11268
 
2.0%
A 8449
 
1.5%
D 5150
 
0.9%
N 3944
 
0.7%
K 3540
 
0.6%
Other values (16) 10320
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 551548
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 215203
39.0%
S 207543
37.6%
C 38135
 
6.9%
Z 34264
 
6.2%
E 13732
 
2.5%
B 11268
 
2.0%
A 8449
 
1.5%
D 5150
 
0.9%
N 3944
 
0.7%
K 3540
 
0.6%
Other values (16) 10320
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 551548
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 215203
39.0%
S 207543
37.6%
C 38135
 
6.9%
Z 34264
 
6.2%
E 13732
 
2.5%
B 11268
 
2.0%
A 8449
 
1.5%
D 5150
 
0.9%
N 3944
 
0.7%
K 3540
 
0.6%
Other values (16) 10320
 
1.9%
Distinct4857
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:54.374137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length23
Mean length13.90963
Min length7

Characters and Unicode

Total characters3875042
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1526 ?
Unique (%)0.5%

Sample

1st rowChristopher Shepard
2nd rowAshley Cruz
3rd rowJeremy Colon
4th rowAshley Cruz
5th rowAshley Cruz
ValueCountFrequency (%)
christopher 71518
 
12.8%
shepard 70419
 
12.6%
brown 43095
 
7.7%
ryan 42861
 
7.7%
ashley 27221
 
4.9%
cruz 26182
 
4.7%
michael 15021
 
2.7%
carl 12841
 
2.3%
ramsey 12828
 
2.3%
clark 12320
 
2.2%
Other values (1439) 225443
40.3%
2024-02-15T22:34:54.785463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 392493
 
10.1%
e 333851
 
8.6%
h 308031
 
7.9%
281162
 
7.3%
a 279669
 
7.2%
i 199794
 
5.2%
o 198445
 
5.1%
n 192285
 
5.0%
s 176130
 
4.5%
l 167970
 
4.3%
Other values (44) 1345212
34.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3030966
78.2%
Uppercase Letter 561766
 
14.5%
Space Separator 281162
 
7.3%
Other Punctuation 1148
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 392493
12.9%
e 333851
11.0%
h 308031
10.2%
a 279669
9.2%
i 199794
 
6.6%
o 198445
 
6.5%
n 192285
 
6.3%
s 176130
 
5.8%
l 167970
 
5.5%
p 154036
 
5.1%
Other values (16) 628262
20.7%
Uppercase Letter
ValueCountFrequency (%)
C 142670
25.4%
S 86964
15.5%
R 71124
12.7%
B 49798
 
8.9%
A 36956
 
6.6%
M 34928
 
6.2%
J 20954
 
3.7%
T 18063
 
3.2%
D 15996
 
2.8%
K 12112
 
2.2%
Other values (16) 72201
12.9%
Space Separator
ValueCountFrequency (%)
281162
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3592732
92.7%
Common 282310
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 392493
 
10.9%
e 333851
 
9.3%
h 308031
 
8.6%
a 279669
 
7.8%
i 199794
 
5.6%
o 198445
 
5.5%
n 192285
 
5.4%
s 176130
 
4.9%
l 167970
 
4.7%
p 154036
 
4.3%
Other values (42) 1190028
33.1%
Common
ValueCountFrequency (%)
281162
99.6%
. 1148
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3875042
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 392493
 
10.1%
e 333851
 
8.6%
h 308031
 
7.9%
281162
 
7.3%
a 279669
 
7.2%
i 199794
 
5.2%
o 198445
 
5.1%
n 192285
 
5.0%
s 176130
 
4.5%
l 167970
 
4.3%
Other values (44) 1345212
34.7%

DESTINATION_STATE
Text

MISSING 

Distinct112
Distinct (%)< 0.1%
Missing3286
Missing (%)1.2%
Memory size2.1 MiB
2024-02-15T22:34:54.956922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9999818
Min length1

Characters and Unicode

Total characters550597
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowOK
2nd rowCA
3rd rowCA
4th rowCA
5th rowCA
ValueCountFrequency (%)
ca 122454
44.5%
xx 59558
21.6%
tx 43569
 
15.8%
ga 17178
 
6.2%
ok 4920
 
1.8%
cz 4293
 
1.6%
la 3793
 
1.4%
az 2269
 
0.8%
pa 1057
 
0.4%
oh 970
 
0.4%
Other values (102) 15240
 
5.5%
2024-02-15T22:34:55.232369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
X 162780
29.6%
A 149185
27.1%
C 128469
23.3%
T 44514
 
8.1%
G 17657
 
3.2%
O 7095
 
1.3%
Z 6592
 
1.2%
K 5916
 
1.1%
L 5463
 
1.0%
N 3164
 
0.6%
Other values (17) 19762
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 550557
> 99.9%
Currency Symbol 40
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
X 162780
29.6%
A 149185
27.1%
C 128469
23.3%
T 44514
 
8.1%
G 17657
 
3.2%
O 7095
 
1.3%
Z 6592
 
1.2%
K 5916
 
1.1%
L 5463
 
1.0%
N 3164
 
0.6%
Other values (16) 19722
 
3.6%
Currency Symbol
ValueCountFrequency (%)
$ 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 550557
> 99.9%
Common 40
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
X 162780
29.6%
A 149185
27.1%
C 128469
23.3%
T 44514
 
8.1%
G 17657
 
3.2%
O 7095
 
1.3%
Z 6592
 
1.2%
K 5916
 
1.1%
L 5463
 
1.0%
N 3164
 
0.6%
Other values (16) 19722
 
3.6%
Common
ValueCountFrequency (%)
$ 40
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 550597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
X 162780
29.6%
A 149185
27.1%
C 128469
23.3%
T 44514
 
8.1%
G 17657
 
3.2%
O 7095
 
1.3%
Z 6592
 
1.2%
K 5916
 
1.1%
L 5463
 
1.0%
N 3164
 
0.6%
Other values (17) 19762
 
3.6%

DESTINATION_ZIP
Unsupported

REJECTED  UNSUPPORTED 

Missing153
Missing (%)0.1%
Memory size2.1 MiB

IO_CODE
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
1
105345 
2
104060 
4
39929 
3
29253 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters278587
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

Length

2024-02-15T22:34:55.366750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T22:34:55.465623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

Most occurring characters

ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 278587
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
Common 278587
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278587
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105345
37.8%
2 104060
37.4%
4 39929
 
14.3%
3 29253
 
10.5%

IO_CODE_DESCRIPTION
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Inbound
105345 
Outbound
104060 
Third Party
39929 
Interfacility
29253 

Length

Max length13
Median length11
Mean length8.5768647
Min length7

Characters and Unicode

Total characters2389403
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowInbound
2nd rowInbound
3rd rowInbound
4th rowInbound
5th rowInbound

Common Values

ValueCountFrequency (%)
Inbound 105345
37.8%
Outbound 104060
37.4%
Third Party 39929
 
14.3%
Interfacility 29253
 
10.5%

Length

2024-02-15T22:34:55.588478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-15T22:34:55.707265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
inbound 105345
33.1%
outbound 104060
32.7%
third 39929
 
12.5%
party 39929
 
12.5%
interfacility 29253
 
9.2%

Most occurring characters

ValueCountFrequency (%)
n 344003
14.4%
u 313465
13.1%
d 249334
10.4%
b 209405
8.8%
o 209405
8.8%
t 202495
8.5%
I 134598
 
5.6%
r 109111
 
4.6%
O 104060
 
4.4%
i 98435
 
4.1%
Other values (10) 415092
17.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2030958
85.0%
Uppercase Letter 318516
 
13.3%
Space Separator 39929
 
1.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 344003
16.9%
u 313465
15.4%
d 249334
12.3%
b 209405
10.3%
o 209405
10.3%
t 202495
10.0%
r 109111
 
5.4%
i 98435
 
4.8%
a 69182
 
3.4%
y 69182
 
3.4%
Other values (5) 156941
7.7%
Uppercase Letter
ValueCountFrequency (%)
I 134598
42.3%
O 104060
32.7%
T 39929
 
12.5%
P 39929
 
12.5%
Space Separator
ValueCountFrequency (%)
39929
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2349474
98.3%
Common 39929
 
1.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 344003
14.6%
u 313465
13.3%
d 249334
10.6%
b 209405
8.9%
o 209405
8.9%
t 202495
8.6%
I 134598
 
5.7%
r 109111
 
4.6%
O 104060
 
4.4%
i 98435
 
4.2%
Other values (9) 375163
16.0%
Common
ValueCountFrequency (%)
39929
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2389403
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 344003
14.4%
u 313465
13.1%
d 249334
10.4%
b 209405
8.8%
o 209405
8.8%
t 202495
8.5%
I 134598
 
5.6%
r 109111
 
4.6%
O 104060
 
4.4%
i 98435
 
4.1%
Other values (10) 415092
17.4%

MILEAGE
Real number (ℝ)

ZEROS 

Distinct2537
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean600.88452
Minimum0
Maximum4814
Zeros128368
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:55.838484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31
Q31107
95-th percentile2302
Maximum4814
Range4814
Interquartile range (IQR)1107

Descriptive statistics

Standard deviation826.57897
Coefficient of variation (CV)1.3756037
Kurtosis0.22777737
Mean600.88452
Median Absolute Deviation (MAD)31
Skewness1.1795863
Sum1.6739862 × 108
Variance683232.79
MonotonicityNot monotonic
2024-02-15T22:34:55.981442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 128368
46.1%
1102 6804
 
2.4%
1345 4079
 
1.5%
2118 2864
 
1.0%
112 2576
 
0.9%
352 2507
 
0.9%
1024 2410
 
0.9%
117 2400
 
0.9%
1474 2390
 
0.9%
1392 2313
 
0.8%
Other values (2527) 121876
43.7%
ValueCountFrequency (%)
0 128368
46.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 963
 
0.3%
5 6
 
< 0.1%
6 79
 
< 0.1%
7 56
 
< 0.1%
8 384
 
0.1%
9 1596
 
0.6%
10 694
 
0.2%
ValueCountFrequency (%)
4814 2
 
< 0.1%
4653 3
 
< 0.1%
4651 12
< 0.1%
4541 2
 
< 0.1%
4432 2
 
< 0.1%
4394 7
 
< 0.1%
4359 7
 
< 0.1%
4332 2
 
< 0.1%
4320 19
< 0.1%
4262 8
< 0.1%
Distinct1962
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:56.194335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length8.2064238
Min length3

Characters and Unicode

Total characters2286203
Distinct characters39
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique371 ?
Unique (%)0.1%

Sample

1st rowMOUNTAIN TOP
2nd rowSHAKOPEE
3rd rowALEXANDER
4th rowFLORENCE
5th rowFLORENCE
ValueCountFrequency (%)
san 50201
 
13.7%
diego 48853
 
13.4%
fairburn 36962
 
10.1%
tijuana 27079
 
7.4%
desoto 25277
 
6.9%
ontario 6934
 
1.9%
mabank 6763
 
1.9%
houston 5434
 
1.5%
channelview 4857
 
1.3%
south 4690
 
1.3%
Other values (2006) 148333
40.6%
2024-02-15T22:34:56.575948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 279592
12.2%
N 242970
10.6%
O 217165
 
9.5%
I 185174
 
8.1%
E 182973
 
8.0%
R 150669
 
6.6%
S 133085
 
5.8%
T 117794
 
5.2%
D 106385
 
4.7%
U 93625
 
4.1%
Other values (29) 576771
25.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 2197926
96.1%
Space Separator 87888
 
3.8%
Decimal Number 204
 
< 0.1%
Dash Punctuation 137
 
< 0.1%
Other Punctuation 34
 
< 0.1%
Open Punctuation 11
 
< 0.1%
Close Punctuation 3
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 279592
12.7%
N 242970
11.1%
O 217165
9.9%
I 185174
 
8.4%
E 182973
 
8.3%
R 150669
 
6.9%
S 133085
 
6.1%
T 117794
 
5.4%
D 106385
 
4.8%
U 93625
 
4.3%
Other values (16) 488494
22.2%
Decimal Number
ValueCountFrequency (%)
1 85
41.7%
0 48
23.5%
3 38
18.6%
2 31
 
15.2%
6 2
 
1.0%
Other Punctuation
ValueCountFrequency (%)
, 20
58.8%
. 7
 
20.6%
/ 5
 
14.7%
" 2
 
5.9%
Space Separator
ValueCountFrequency (%)
87888
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 137
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2197926
96.1%
Common 88277
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 279592
12.7%
N 242970
11.1%
O 217165
9.9%
I 185174
 
8.4%
E 182973
 
8.3%
R 150669
 
6.9%
S 133085
 
6.1%
T 117794
 
5.4%
D 106385
 
4.8%
U 93625
 
4.3%
Other values (16) 488494
22.2%
Common
ValueCountFrequency (%)
87888
99.6%
- 137
 
0.2%
1 85
 
0.1%
0 48
 
0.1%
3 38
 
< 0.1%
2 31
 
< 0.1%
, 20
 
< 0.1%
( 11
 
< 0.1%
. 7
 
< 0.1%
/ 5
 
< 0.1%
Other values (3) 7
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2286203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 279592
12.2%
N 242970
10.6%
O 217165
 
9.5%
I 185174
 
8.1%
E 182973
 
8.0%
R 150669
 
6.6%
S 133085
 
5.8%
T 117794
 
5.2%
D 106385
 
4.7%
U 93625
 
4.1%
Other values (29) 576771
25.2%
Distinct84
Distinct (%)< 0.1%
Missing1714
Missing (%)0.6%
Memory size2.1 MiB
2024-02-15T22:34:56.725554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters553746
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowUS
2nd rowUS
3rd rowUS
4th rowUS
5th rowUS
ValueCountFrequency (%)
us 218086
78.8%
mx 28519
 
10.3%
ca 7642
 
2.8%
cz 3050
 
1.1%
jp 2600
 
0.9%
de 2222
 
0.8%
it 1907
 
0.7%
tr 1793
 
0.6%
gb 1489
 
0.5%
uk 1223
 
0.4%
Other values (74) 8342
 
3.0%
2024-02-15T22:34:56.965549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
U 219855
39.7%
S 219740
39.7%
M 28813
 
5.2%
X 28523
 
5.2%
C 11618
 
2.1%
A 9141
 
1.7%
T 4665
 
0.8%
E 3854
 
0.7%
I 3165
 
0.6%
Z 3083
 
0.6%
Other values (16) 21289
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 553746
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
U 219855
39.7%
S 219740
39.7%
M 28813
 
5.2%
X 28523
 
5.2%
C 11618
 
2.1%
A 9141
 
1.7%
T 4665
 
0.8%
E 3854
 
0.7%
I 3165
 
0.6%
Z 3083
 
0.6%
Other values (16) 21289
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 553746
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
U 219855
39.7%
S 219740
39.7%
M 28813
 
5.2%
X 28523
 
5.2%
C 11618
 
2.1%
A 9141
 
1.7%
T 4665
 
0.8%
E 3854
 
0.7%
I 3165
 
0.6%
Z 3083
 
0.6%
Other values (16) 21289
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 553746
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
U 219855
39.7%
S 219740
39.7%
M 28813
 
5.2%
X 28523
 
5.2%
C 11618
 
2.1%
A 9141
 
1.7%
T 4665
 
0.8%
E 3854
 
0.7%
I 3165
 
0.6%
Z 3083
 
0.6%
Other values (16) 21289
 
3.8%
Distinct4931
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Memory size2.1 MiB
2024-02-15T22:34:57.194573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length13.033458
Min length7

Characters and Unicode

Total characters3630939
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1600 ?
Unique (%)0.6%

Sample

1st rowDerrick Castro
2nd rowJason Harvey
3rd rowJulia Nielsen
4th rowRichard Wright
5th rowRichard Wright
ValueCountFrequency (%)
nguyen 36781
 
6.5%
kristie 36690
 
6.5%
gregory 23676
 
4.2%
avery 23153
 
4.1%
sanchez 16425
 
2.9%
roy 16361
 
2.9%
mary 14356
 
2.5%
calderon 14201
 
2.5%
lisa 13354
 
2.4%
eric 13104
 
2.3%
Other values (1451) 355436
63.1%
2024-02-15T22:34:57.554645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 367138
 
10.1%
r 303769
 
8.4%
284951
 
7.8%
a 258688
 
7.1%
n 236268
 
6.5%
i 221608
 
6.1%
o 199356
 
5.5%
y 176315
 
4.9%
l 160662
 
4.4%
s 150508
 
4.1%
Other values (44) 1271676
35.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2775280
76.4%
Uppercase Letter 568011
 
15.6%
Space Separator 284951
 
7.8%
Other Punctuation 2697
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 367138
13.2%
r 303769
10.9%
a 258688
9.3%
n 236268
8.5%
i 221608
 
8.0%
o 199356
 
7.2%
y 176315
 
6.4%
l 160662
 
5.8%
s 150508
 
5.4%
t 139637
 
5.0%
Other values (16) 561331
20.2%
Uppercase Letter
ValueCountFrequency (%)
S 57110
 
10.1%
A 52236
 
9.2%
K 45669
 
8.0%
N 43817
 
7.7%
C 41718
 
7.3%
M 37843
 
6.7%
L 35071
 
6.2%
H 34390
 
6.1%
B 32625
 
5.7%
G 31509
 
5.5%
Other values (16) 156023
27.5%
Space Separator
ValueCountFrequency (%)
284951
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2697
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3343291
92.1%
Common 287648
 
7.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 367138
 
11.0%
r 303769
 
9.1%
a 258688
 
7.7%
n 236268
 
7.1%
i 221608
 
6.6%
o 199356
 
6.0%
y 176315
 
5.3%
l 160662
 
4.8%
s 150508
 
4.5%
t 139637
 
4.2%
Other values (42) 1129342
33.8%
Common
ValueCountFrequency (%)
284951
99.1%
. 2697
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3630939
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 367138
 
10.1%
r 303769
 
8.4%
284951
 
7.8%
a 258688
 
7.1%
n 236268
 
6.5%
i 221608
 
6.1%
o 199356
 
5.5%
y 176315
 
4.9%
l 160662
 
4.4%
s 150508
 
4.1%
Other values (44) 1271676
35.0%

ORIGIN_STATE
Text

MISSING 

Distinct100
Distinct (%)< 0.1%
Missing6996
Missing (%)2.5%
Memory size2.1 MiB
2024-02-15T22:34:57.718544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters543182
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowPA
2nd rowMN
3rd rowAR
4th rowKY
5th rowKY
ValueCountFrequency (%)
ca 84115
31.0%
tx 50417
18.6%
ga 40728
15.0%
xx 16992
 
6.3%
bc 14238
 
5.2%
mx 12638
 
4.7%
on 5657
 
2.1%
oh 4832
 
1.8%
ok 4565
 
1.7%
az 4302
 
1.6%
Other values (90) 33107
 
12.2%
2024-02-15T22:34:57.976519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 137381
25.3%
C 103359
19.0%
X 97039
17.9%
T 53216
 
9.8%
G 40821
 
7.5%
M 18481
 
3.4%
O 16819
 
3.1%
B 14998
 
2.8%
N 14073
 
2.6%
I 7805
 
1.4%
Other values (16) 39190
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 543182
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 137381
25.3%
C 103359
19.0%
X 97039
17.9%
T 53216
 
9.8%
G 40821
 
7.5%
M 18481
 
3.4%
O 16819
 
3.1%
B 14998
 
2.8%
N 14073
 
2.6%
I 7805
 
1.4%
Other values (16) 39190
 
7.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 543182
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 137381
25.3%
C 103359
19.0%
X 97039
17.9%
T 53216
 
9.8%
G 40821
 
7.5%
M 18481
 
3.4%
O 16819
 
3.1%
B 14998
 
2.8%
N 14073
 
2.6%
I 7805
 
1.4%
Other values (16) 39190
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 543182
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 137381
25.3%
C 103359
19.0%
X 97039
17.9%
T 53216
 
9.8%
G 40821
 
7.5%
M 18481
 
3.4%
O 16819
 
3.1%
B 14998
 
2.8%
N 14073
 
2.6%
I 7805
 
1.4%
Other values (16) 39190
 
7.2%

ORIGIN_ZIP
Unsupported

REJECTED  UNSUPPORTED 

Missing148
Missing (%)0.1%
Memory size2.1 MiB
Distinct1045
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2020-01-06 00:00:00
Maximum2024-02-06 00:00:00
2024-02-15T22:34:58.122173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:58.261430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PRO_NUMBER2
Real number (ℝ)

MISSING 

Distinct147178
Distinct (%)54.7%
Missing9317
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean5.0010035 × 1012
Minimum1.3514963 × 108
Maximum9.9999377 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:58.405789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.3514963 × 108
5-th percentile4.9159138 × 1011
Q12.4984058 × 1012
median5.0039166 × 1012
Q37.5013252 × 1012
95-th percentile9.5041179 × 1012
Maximum9.9999377 × 1012
Range9.9998025 × 1012
Interquartile range (IQR)5.0029195 × 1012

Descriptive statistics

Standard deviation2.8890583 × 1012
Coefficient of variation (CV)0.57769573
Kurtosis-1.2002091
Mean5.0010035 × 1012
Median Absolute Deviation (MAD)2.5010961 × 1012
Skewness-0.00099756212
Sum1.3466202 × 1018
Variance8.3466581 × 1024
MonotonicityNot monotonic
2024-02-15T22:34:58.543839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.067741336 × 101219
 
< 0.1%
5.450729213 × 101212
 
< 0.1%
5.674512345 × 101212
 
< 0.1%
1.438908826 × 101212
 
< 0.1%
6.006171343 × 101212
 
< 0.1%
7.200775697 × 101212
 
< 0.1%
9.554701198 × 101212
 
< 0.1%
6.907628391 × 101112
 
< 0.1%
4.302833979 × 101212
 
< 0.1%
3.424880772 × 101112
 
< 0.1%
Other values (147168) 269143
96.6%
(Missing) 9317
 
3.3%
ValueCountFrequency (%)
135149629 1
< 0.1%
175591785 1
< 0.1%
213976710 1
< 0.1%
402685065 2
< 0.1%
479960157 1
< 0.1%
497546968 1
< 0.1%
523138549 1
< 0.1%
553233603 1
< 0.1%
655018917 1
< 0.1%
690112892 1
< 0.1%
ValueCountFrequency (%)
9.999937663 × 10121
< 0.1%
9.999858228 × 10121
< 0.1%
9.999853088 × 10121
< 0.1%
9.999796206 × 10121
< 0.1%
9.999718983 × 10121
< 0.1%
9.999698537 × 10121
< 0.1%
9.999620617 × 10121
< 0.1%
9.999465428 × 10121
< 0.1%
9.999380799 × 10121
< 0.1%
9.99926455 × 10121
< 0.1%
Distinct5008
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
2024-02-15T22:34:58.805057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length19
Mean length16.102833
Min length1

Characters and Unicode

Total characters4486040
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1625 ?
Unique (%)0.6%

Sample

1st rowSOLAR TURBINES
2nd rowSOLAR TURBINES KEARN
3rd rowTURBOTEC SOLAR TURBI
4th rowSOLAR TURBINES KEARN
5th rowSOLAR TURBINES KEARN
ValueCountFrequency (%)
solar 182693
26.7%
turbines 175915
25.7%
harbo 42502
 
6.2%
kearn 27383
 
4.0%
inc 14444
 
2.1%
overh 12819
 
1.9%
turbotec 9780
 
1.4%
c/o 7707
 
1.1%
ups-scs 7675
 
1.1%
reman 6555
 
1.0%
Other values (4390) 197536
28.8%
2024-02-15T22:34:59.209259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 567649
12.7%
S 438447
9.8%
406555
9.1%
A 359775
 
8.0%
O 340462
 
7.6%
E 330377
 
7.4%
N 292475
 
6.5%
I 274393
 
6.1%
T 264994
 
5.9%
B 254506
 
5.7%
Other values (37) 956407
21.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4058404
90.5%
Space Separator 406555
 
9.1%
Dash Punctuation 10931
 
0.2%
Other Punctuation 9503
 
0.2%
Decimal Number 624
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 567649
14.0%
S 438447
10.8%
A 359775
8.9%
O 340462
8.4%
E 330377
8.1%
N 292475
7.2%
I 274393
 
6.8%
T 264994
 
6.5%
B 254506
 
6.3%
L 243451
 
6.0%
Other values (16) 691875
17.0%
Decimal Number
ValueCountFrequency (%)
2 232
37.2%
3 162
26.0%
0 64
 
10.3%
1 54
 
8.7%
8 35
 
5.6%
6 27
 
4.3%
4 21
 
3.4%
5 20
 
3.2%
9 5
 
0.8%
7 4
 
0.6%
Other Punctuation
ValueCountFrequency (%)
/ 8109
85.3%
& 737
 
7.8%
% 638
 
6.7%
. 13
 
0.1%
' 3
 
< 0.1%
; 3
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 15
68.2%
[ 7
31.8%
Space Separator
ValueCountFrequency (%)
406555
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10931
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4058404
90.5%
Common 427636
 
9.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 567649
14.0%
S 438447
10.8%
A 359775
8.9%
O 340462
8.4%
E 330377
8.1%
N 292475
7.2%
I 274393
 
6.8%
T 264994
 
6.5%
B 254506
 
6.3%
L 243451
 
6.0%
Other values (16) 691875
17.0%
Common
ValueCountFrequency (%)
406555
95.1%
- 10931
 
2.6%
/ 8109
 
1.9%
& 737
 
0.2%
% 638
 
0.1%
2 232
 
0.1%
3 162
 
< 0.1%
0 64
 
< 0.1%
1 54
 
< 0.1%
8 35
 
< 0.1%
Other values (11) 119
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4486040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 567649
12.7%
S 438447
9.8%
406555
9.1%
A 359775
 
8.0%
O 340462
 
7.6%
E 330377
 
7.4%
N 292475
 
6.5%
I 274393
 
6.1%
T 264994
 
5.9%
B 254506
 
5.7%
Other values (37) 956407
21.3%
Distinct5101
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Memory size2.1 MiB
2024-02-15T22:34:59.462375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length28
Mean length16.400386
Min length2

Characters and Unicode

Total characters4568918
Distinct characters58
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1701 ?
Unique (%)0.6%

Sample

1st rowWYMAN GORDON
2nd rowROSEMOUNT INC
3rd rowPPG INDUSTRIES
4th rowREGAL BELOIT
5th rowREGAL BELOIT
ValueCountFrequency (%)
solar 113143
 
16.2%
turbines 112488
 
16.2%
inc 42884
 
6.2%
overhaul 23152
 
3.3%
turbotec 22165
 
3.2%
turbo 14594
 
2.1%
tecnologi 14189
 
2.0%
mesa 12853
 
1.8%
kearny 12802
 
1.8%
drive 11951
 
1.7%
Other values (4449) 316056
45.4%
2024-02-15T22:34:59.871333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 484667
10.6%
417909
 
9.1%
E 359138
 
7.9%
S 342833
 
7.5%
O 341627
 
7.5%
A 340089
 
7.4%
I 297961
 
6.5%
T 293624
 
6.4%
N 291025
 
6.4%
L 243436
 
5.3%
Other values (48) 1156609
25.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4123779
90.3%
Space Separator 417909
 
9.1%
Dash Punctuation 13468
 
0.3%
Other Punctuation 13212
 
0.3%
Decimal Number 273
 
< 0.1%
Lowercase Letter 198
 
< 0.1%
Open Punctuation 43
 
< 0.1%
Close Punctuation 36
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
R 484667
11.8%
E 359138
 
8.7%
S 342833
 
8.3%
O 341627
 
8.3%
A 340089
 
8.2%
I 297961
 
7.2%
T 293624
 
7.1%
N 291025
 
7.1%
L 243436
 
5.9%
U 229768
 
5.6%
Other values (16) 899611
21.8%
Lowercase Letter
ValueCountFrequency (%)
i 28
14.1%
a 28
14.1%
n 28
14.1%
l 16
8.1%
o 14
7.1%
j 14
7.1%
c 14
7.1%
m 14
7.1%
d 14
7.1%
r 14
7.1%
Decimal Number
ValueCountFrequency (%)
0 110
40.3%
2 36
 
13.2%
1 36
 
13.2%
3 35
 
12.8%
6 15
 
5.5%
4 13
 
4.8%
5 12
 
4.4%
8 6
 
2.2%
9 5
 
1.8%
7 5
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 10984
83.1%
& 1503
 
11.4%
. 421
 
3.2%
% 281
 
2.1%
, 12
 
0.1%
' 6
 
< 0.1%
; 5
 
< 0.1%
Space Separator
ValueCountFrequency (%)
417909
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13468
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4123977
90.3%
Common 444941
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
R 484667
11.8%
E 359138
 
8.7%
S 342833
 
8.3%
O 341627
 
8.3%
A 340089
 
8.2%
I 297961
 
7.2%
T 293624
 
7.1%
N 291025
 
7.1%
L 243436
 
5.9%
U 229768
 
5.6%
Other values (27) 899809
21.8%
Common
ValueCountFrequency (%)
417909
93.9%
- 13468
 
3.0%
/ 10984
 
2.5%
& 1503
 
0.3%
. 421
 
0.1%
% 281
 
0.1%
0 110
 
< 0.1%
( 43
 
< 0.1%
) 36
 
< 0.1%
2 36
 
< 0.1%
Other values (11) 150
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4568918
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
R 484667
10.6%
417909
 
9.1%
E 359138
 
7.9%
S 342833
 
7.5%
O 341627
 
7.5%
A 340089
 
7.4%
I 297961
 
6.5%
T 293624
 
6.4%
N 291025
 
6.4%
L 243436
 
5.3%
Other values (48) 1156609
25.3%

SHIPPER_NAME
Categorical

IMBALANCE 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
SOLAR TURBINES - SAN DIEGO
184939 
SOLAR TURBINES - DESOTO
34444 
SOLAR TURBINES-FAIRBURN
23765 
SOLAR TURBINES - MABANK
 
12972
SOLAR TURBINES-AIR SAN DIEGO
 
7240
Other values (14)
 
15227

Length

Max length30
Median length26
Mean length25.249348
Min length23

Characters and Unicode

Total characters7034140
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSOLAR TURBINES - SAN DIEGO
2nd rowSOLAR TURBINES - SAN DIEGO
3rd rowSOLAR TURBINES - SAN DIEGO
4th rowSOLAR TURBINES - SAN DIEGO
5th rowSOLAR TURBINES - SAN DIEGO

Common Values

ValueCountFrequency (%)
SOLAR TURBINES - SAN DIEGO 184939
66.4%
SOLAR TURBINES - DESOTO 34444
 
12.4%
SOLAR TURBINES-FAIRBURN 23765
 
8.5%
SOLAR TURBINES - MABANK 12972
 
4.7%
SOLAR TURBINES-AIR SAN DIEGO 7240
 
2.6%
SOLAR TURBINES-TURBO FAB 4466
 
1.6%
SOLAR TURBINES-HARBOR DRIVE 3431
 
1.2%
SOLAR TURBINES-TURBOTEC 2793
 
1.0%
SOLAR TURBINES-AIR FAIRBURN 2691
 
1.0%
SOLAR TURBINES-KEARNY MESA 786
 
0.3%
Other values (9) 1060
 
0.4%

Length

2024-02-15T22:35:00.036897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
solar 278587
22.6%
232355
18.8%
turbines 232355
18.8%
san 192179
15.6%
diego 192179
15.6%
desoto 34628
 
2.8%
turbines-fairburn 23765
 
1.9%
mabank 13113
 
1.1%
turbines-air 10640
 
0.9%
turbines-turbo 4466
 
0.4%
Other values (20) 19854
 
1.6%

Most occurring characters

ValueCountFrequency (%)
955534
13.6%
S 784977
11.2%
R 641073
9.1%
O 551638
7.8%
A 544294
 
7.7%
E 513883
 
7.3%
I 511445
 
7.3%
N 511428
 
7.3%
B 333837
 
4.7%
T 323315
 
4.6%
Other values (14) 1362716
19.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 5800019
82.5%
Space Separator 955534
 
13.6%
Dash Punctuation 278587
 
4.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 784977
13.5%
R 641073
11.1%
O 551638
9.5%
A 544294
9.4%
E 513883
8.9%
I 511445
8.8%
N 511428
8.8%
B 333837
5.8%
T 323315
5.6%
U 312478
 
5.4%
Other values (12) 771651
13.3%
Space Separator
ValueCountFrequency (%)
955534
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 278587
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 5800019
82.5%
Common 1234121
 
17.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 784977
13.5%
R 641073
11.1%
O 551638
9.5%
A 544294
9.4%
E 513883
8.9%
I 511445
8.8%
N 511428
8.8%
B 333837
5.8%
T 323315
5.6%
U 312478
 
5.4%
Other values (12) 771651
13.3%
Common
ValueCountFrequency (%)
955534
77.4%
- 278587
 
22.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7034140
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
955534
13.6%
S 784977
11.2%
R 641073
9.1%
O 551638
7.8%
A 544294
 
7.7%
E 513883
 
7.3%
I 511445
 
7.3%
N 511428
 
7.3%
B 333837
 
4.7%
T 323315
 
4.6%
Other values (14) 1362716
19.4%
Distinct1466
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
Minimum2020-01-01 00:00:00
Maximum2024-02-02 00:00:00
2024-02-15T22:35:00.176599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:35:00.325655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

SHIP_WEIGHT
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct13633
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3400.4959
Minimum0
Maximum1421500
Zeros51581
Zeros (%)18.5%
Negative0
Negative (%)0.0%
Memory size2.1 MiB
2024-02-15T22:35:00.467282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q195
median401.234
Q31492.504
95-th percentile17815
Maximum1421500
Range1421500
Interquartile range (IQR)1397.504

Descriptive statistics

Standard deviation15863.319
Coefficient of variation (CV)4.6650017
Kurtosis1291.2691
Mean3400.4959
Median Absolute Deviation (MAD)401.234
Skewness25.842011
Sum9.4733395 × 108
Variance2.516449 × 108
MonotonicityNot monotonic
2024-02-15T22:35:00.620988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 51581
 
18.5%
1 2559
 
0.9%
2.205 1937
 
0.7%
100 1235
 
0.4%
200 1106
 
0.4%
300 1094
 
0.4%
150 1021
 
0.4%
400 965
 
0.3%
1500 938
 
0.3%
500 903
 
0.3%
Other values (13623) 215248
77.3%
ValueCountFrequency (%)
0 51581
18.5%
1 2559
 
0.9%
2 145
 
0.1%
2.205 1937
 
0.7%
3 88
 
< 0.1%
4 128
 
< 0.1%
4.409 563
 
0.2%
5 132
 
< 0.1%
6 51
 
< 0.1%
6.614 322
 
0.1%
ValueCountFrequency (%)
1421500 1
 
< 0.1%
1158465.326 6
< 0.1%
973639 2
 
< 0.1%
971503.29 1
 
< 0.1%
672356.538 4
< 0.1%
663641.813 5
< 0.1%
532968 3
 
< 0.1%
527277.208 2
 
< 0.1%
500683.299 9
< 0.1%
454129.078 5
< 0.1%

SOLAR_MODE
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing234
Missing (%)0.1%
Memory size2.1 MiB
LTL
119510 
AE
71562 
TL
26326 
FF
22269 
CU
22231 
Other values (7)
16455 

Length

Max length3
Median length2
Mean length2.4519154
Min length2

Characters and Unicode

Total characters682498
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLTL
2nd rowLTL
3rd rowLTL
4th rowLTL
5th rowLTL

Common Values

ValueCountFrequency (%)
LTL 119510
42.9%
AE 71562
25.7%
TL 26326
 
9.4%
FF 22269
 
8.0%
CU 22231
 
8.0%
HH 6998
 
2.5%
OCN 6251
 
2.2%
CR 2939
 
1.1%
PA 164
 
0.1%
MV 40
 
< 0.1%
Other values (2) 63
 
< 0.1%
(Missing) 234
 
0.1%

Length

2024-02-15T22:35:00.762583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ltl 119510
42.9%
ae 71562
25.7%
tl 26326
 
9.5%
ff 22269
 
8.0%
cu 22231
 
8.0%
hh 6998
 
2.5%
ocn 6251
 
2.2%
cr 2939
 
1.1%
pa 164
 
0.1%
mv 40
 
< 0.1%
Other values (2) 63
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
L 265346
38.9%
T 145836
21.4%
A 71757
 
10.5%
E 71562
 
10.5%
F 44538
 
6.5%
C 31421
 
4.6%
U 22231
 
3.3%
H 13996
 
2.1%
O 6251
 
0.9%
N 6251
 
0.9%
Other values (5) 3309
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 682498
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
L 265346
38.9%
T 145836
21.4%
A 71757
 
10.5%
E 71562
 
10.5%
F 44538
 
6.5%
C 31421
 
4.6%
U 22231
 
3.3%
H 13996
 
2.1%
O 6251
 
0.9%
N 6251
 
0.9%
Other values (5) 3309
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 682498
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 265346
38.9%
T 145836
21.4%
A 71757
 
10.5%
E 71562
 
10.5%
F 44538
 
6.5%
C 31421
 
4.6%
U 22231
 
3.3%
H 13996
 
2.1%
O 6251
 
0.9%
N 6251
 
0.9%
Other values (5) 3309
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 682498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 265346
38.9%
T 145836
21.4%
A 71757
 
10.5%
E 71562
 
10.5%
F 44538
 
6.5%
C 31421
 
4.6%
U 22231
 
3.3%
H 13996
 
2.1%
O 6251
 
0.9%
N 6251
 
0.9%
Other values (5) 3309
 
0.5%

Interactions

2024-02-15T22:34:45.534516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:38.856331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.979091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.991096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.090953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.332878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.372505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.684269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.010085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.129456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.140345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.228552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.482681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.516099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.836339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.151875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.265169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.282502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.405008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.620211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.658206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.986542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.308940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.417142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.433374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.604108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.776113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.951375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:46.138173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.503794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.553620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.575976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.785106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.922329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.090156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:46.293793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.670073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.701786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.742290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:42.969195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.065925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.237917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:46.446059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:39.823252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:40.845988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:41.910032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:43.161597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:44.216367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-15T22:34:45.385646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-02-15T22:35:00.865355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ACCESSORIAL_AMOUNTACCESSORIAL_CHARGE_AMOUNTAMOUNT_PAIDBILL_TYPECARRIER_MODE_DESCRIPTIONIO_CODEIO_CODE_DESCRIPTIONMILEAGEPRO_NUMBER2SHIPPER_NAMESHIP_WEIGHTSOLAR_MODEUnnamed: 0
ACCESSORIAL_AMOUNT1.0000.6000.8900.0480.0060.0170.017-0.475-0.0050.0000.0120.0530.056
ACCESSORIAL_CHARGE_AMOUNT0.6001.0000.5760.0900.0130.0140.014-0.036-0.0010.0110.2160.0640.050
AMOUNT_PAID0.8900.5761.0000.0270.0170.0220.022-0.294-0.0020.0140.2850.0570.088
BILL_TYPE0.0480.0900.0271.0000.0350.2500.2500.4160.0070.2620.5990.5150.164
CARRIER_MODE_DESCRIPTION0.0060.0130.0170.0351.0000.1530.1530.256-0.0010.3430.0840.415-0.145
IO_CODE0.0170.0140.0220.2500.1531.0001.000-0.053-0.0030.2740.0390.3990.824
IO_CODE_DESCRIPTION0.0170.0140.0220.2500.1531.0001.000-0.064-0.0030.2740.0950.3990.943
MILEAGE-0.475-0.036-0.2940.4160.256-0.053-0.0641.0000.0080.1610.3650.193-0.078
PRO_NUMBER2-0.005-0.001-0.0020.007-0.001-0.003-0.0030.0081.0000.0100.0020.010-0.003
SHIPPER_NAME0.0000.0110.0140.2620.3430.2740.2740.1610.0101.000-0.0670.177-0.051
SHIP_WEIGHT0.0120.2160.2850.5990.0840.0390.0950.3650.002-0.0671.0000.0640.068
SOLAR_MODE0.0530.0640.0570.5150.4150.3990.3990.1930.0100.1770.0641.000-0.112
Unnamed: 00.0560.0500.0880.164-0.1450.8240.943-0.078-0.003-0.0510.068-0.1121.000

Missing values

2024-02-15T22:34:46.806276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-15T22:34:47.691722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-15T22:34:48.923625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0ACCESSORIAL_AMOUNTACCESSORIAL_CHARGE_AMOUNTACCESSORIAL_CHARGE_CODEACCESSORIAL_CHARGE_DESCRIPTIONAMOUNT_PAIDBILL_DATEBILL_TYPECARRIER_MODE_DESCRIPTIONCARRIER_NAME2DESTINATION_CITYDESTINATION_COUNTRY_CODEDESTINATION_NAME2DESTINATION_STATEDESTINATION_ZIPIO_CODEIO_CODE_DESCRIPTIONMILEAGEORIGIN_CITYORIGIN_COUNTRY_CODEORIGIN_NAME2ORIGIN_STATEORIGIN_ZIPPROCESS_DATEPRO_NUMBER2RECEIVERSHIPPERSHIPPER_NAMESHIP_DATESHIP_WEIGHTSOLAR_MODE
00116.23644116.23644FSFUEL SURCHARGE784.274042021-09-28LINEHAULMOTORYoung LtdBROKEN ARROWUSChristopher ShepardOK740121Inbound1237MOUNTAIN TOPUSDerrick CastroPA187072021-10-123.186389e+12SOLAR TURBINESWYMAN GORDONSOLAR TURBINES - SAN DIEGO2021-09-282830.0LTL
1132.7711632.77116FSFUEL SURCHARGE221.093642021-09-24LINEHAULMOTORYoung LtdSAN DIEGOUSAshley CruzCA921231Inbound1899SHAKOPEEUSJason HarveyMN553792021-10-127.564392e+12SOLAR TURBINES KEARNROSEMOUNT INCSOLAR TURBINES - SAN DIEGO2021-09-24549.0LTL
2248.3420638.48706FSFUEL SURCHARGE268.292522021-10-08LINEHAULMOTORYoung LtdSAN DIEGOUSJeremy ColonCA921541Inbound1640ALEXANDERUSJulia NielsenAR720022021-10-193.416653e+12TURBOTEC SOLAR TURBIPPG INDUSTRIESSOLAR TURBINES - SAN DIEGO2021-10-08688.0LTL
3322.2591622.25916FSFUEL SURCHARGE151.688162021-09-08LINEHAULMOTORYoung LtdSAN DIEGOUSAshley CruzCA921231Inbound2166FLORENCEUSRichard WrightKY410422021-10-125.769524e+12SOLAR TURBINES KEARNREGAL BELOITSOLAR TURBINES - SAN DIEGO2021-09-08220.0LTL
4422.5219622.52196FSFUEL SURCHARGE151.950962021-09-24LINEHAULMOTORYoung LtdSAN DIEGOUSAshley CruzCA921231Inbound2166FLORENCEUSRichard WrightKY410422021-10-125.837051e+12SOLAR TURBINES KEARNREGAL BELOITSOLAR TURBINES - SAN DIEGO2021-09-24252.0LTL
5525.3733425.37334FSFUEL SURCHARGE171.227342021-09-30LINEHAULMOTORYoung LtdFAIRBURNUSMichael ClarkGA302131Inbound444FLORENCEUSRichard WrightKY410422021-10-127.619189e+12SOLAR TURBINES INCREGAL BELOITSOLAR TURBINES - SAN DIEGO2021-09-30640.0LTL
6623.8228223.82282FSFUEL SURCHARGE160.728482021-09-28LINEHAULMOTORYoung LtdFAIRBURNUSMichael ClarkGA302131Inbound2118SAN DIEGOUSRoy SanchezCA921542021-10-123.652718e+12SOLAR TURBINES INCSOLAR TURBINESSOLAR TURBINES - SAN DIEGO2021-09-28305.0LTL
7722.6533622.65336FSFUEL SURCHARGE152.082362021-10-05LINEHAULMOTORYoung LtdBROKEN ARROWUSChristopher ShepardOK740121Inbound759WAUKESHAUSRobert JamesWI531862021-10-194.614602e+12SOLAR TURBINESMETAL TEKSOLAR TURBINES - SAN DIEGO2021-10-05300.0LTL
88130.26996130.26996FSFUEL SURCHARGE874.677242021-10-04LINEHAULMOTORYoung LtdBROKEN ARROWUSChristopher ShepardOK740121Inbound971YORKUSMelissa HayesSC297452021-10-193.399017e+12SOLAR TURBINESFOMAS INCSOLAR TURBINES - SAN DIEGO2021-10-043750.0LTL
9921.2736621.27366FSFUEL SURCHARGE142.818662021-10-06LINEHAULMOTORYoung LtdFAIRBURNUSMichael ClarkGA302131Inbound266GREENEVILLEUSWayne MartinTN377432021-10-196.518610e+12SOLAR TURBINES INCDONALDSON CO INSOLAR TURBINES - SAN DIEGO2021-10-061044.0LTL
Unnamed: 0ACCESSORIAL_AMOUNTACCESSORIAL_CHARGE_AMOUNTACCESSORIAL_CHARGE_CODEACCESSORIAL_CHARGE_DESCRIPTIONAMOUNT_PAIDBILL_DATEBILL_TYPECARRIER_MODE_DESCRIPTIONCARRIER_NAME2DESTINATION_CITYDESTINATION_COUNTRY_CODEDESTINATION_NAME2DESTINATION_STATEDESTINATION_ZIPIO_CODEIO_CODE_DESCRIPTIONMILEAGEORIGIN_CITYORIGIN_COUNTRY_CODEORIGIN_NAME2ORIGIN_STATEORIGIN_ZIPPROCESS_DATEPRO_NUMBER2RECEIVERSHIPPERSHIPPER_NAMESHIP_DATESHIP_WEIGHTSOLAR_MODE
27857727857743.3094443.30944FSFUEL SURCHARGE309.039662021-04-08LINEHAULMOTORHodges, Lyons and WeaverTULSAUSJose HarveyOK741174Third Party672SHAKOPEEUSPamela DominguezMN553792021-04-26NaNBERENDSEN FLUIDCONTINENTAL HYDSOLAR TURBINES - SAN DIEGO2021-04-081660.0LTL
27857827857834.1377234.13772FSFUEL SURCHARGE243.576182021-04-05LINEHAULMOTORHodges, Lyons and WeaverCARSONUSDonna LeeCA907454Third Party2603HONEOYE FALLSUSMichael YoungNY144722021-04-26NaNCAL-COAST PACKIGRAVER TECHNOLOSOLAR TURBINES - SAN DIEGO2021-04-05742.0LTL
27857927857960.9827460.98274FSFUEL SURCHARGE437.456882021-04-13LINEHAULMOTORHodges, Lyons and WeaverROCKFORDUSVanessa BlairIL611044Third Party967CORSICANAUSTina PierceTX751102021-04-27NaNDIAL MACHINEOIL CITY IRON WSOLAR TURBINES - SAN DIEGO2021-04-132505.0LTL
27858027858015.1372815.13728FSFUEL SURCHARGE108.037082021-04-09LINEHAULMOTORHodges, Lyons and WeaverPHOENIXUSKathleen FieldsAZ850404Third Party352SAN DIEGOUSRichard DavisCA921542021-04-26NaNCHROMALLOY ARIZCHROMALLOY PGTCSOLAR TURBINES - SAN DIEGO2021-04-09150.0LTL
27858127858157.1327257.13272FSFUEL SURCHARGE407.668502021-04-07LINEHAULMOTORHodges, Lyons and WeaverFOUNTAIN VALLEYUSJavier HarringtonCA927084Third Party1423CORSICANAUSTina PierceTX751102021-04-27NaNOMNI METAL FINIOIL CITY IRON WSOLAR TURBINES - SAN DIEGO2021-04-071973.0LTL
27858227858215.6103215.61032FSFUEL SURCHARGE111.400922021-04-07LINEHAULMOTORHodges, Lyons and WeaverESCONDIDOUSPatricia LloydCA920294Third Party1401CORSICANAUSTina PierceTX751102021-04-27NaNPRICE PRODUCTSOIL CITY IRON WSOLAR TURBINES - SAN DIEGO2021-04-07299.0LTL
27858327858316.2541816.25418FSFUEL SURCHARGE116.551802021-04-12LINEHAULMOTORHodges, Lyons and WeaverPHOENIXUSKathleen FieldsAZ850404Third Party352SAN DIEGOUSHeather MilesCA921542021-04-27NaNCHROMALLOY ARIZUPS SUPPLY CHAISOLAR TURBINES - SAN DIEGO2021-04-12114.0LTL
27858427858415.9913815.99138FSFUEL SURCHARGE114.685922021-04-13LINEHAULMOTORHodges, Lyons and WeaverSAN MARCOSUSBarbara RichardsCA920694Third Party36SAN DIEGOUSHeather MilesCA921542021-04-27NaNBLACK OXIDE SERUPS SUPPLY CHAISOLAR TURBINES - SAN DIEGO2021-04-13268.0LTL
27858527858540.9573815.99138FSFUEL SURCHARGE139.651922021-04-13LINEHAULMOTORHodges, Lyons and WeaverHUNTINGTON PARKUSErika StarkCA902554Third Party120SAN DIEGOUSHeather MilesCA921542021-04-27NaNBODYCOTE THERMAUPS SUPPLY CHAISOLAR TURBINES - SAN DIEGO2021-04-13205.0LTL
27858627858674.2278674.22786FSFUEL SURCHARGE529.594562021-04-08LINEHAULMOTORHodges, Lyons and WeaverCARROLLTONUSNicole BanksTX750064Third Party1633NORTH HAVENUSGrant HarperCT64732021-04-27NaNAMERICAN CRATINSAFT AMERICASOLAR TURBINES - SAN DIEGO2021-04-081865.0LTL